Accurate remote sensing-based crop yield prediction remains a fundamental challenging task due to complex spatial patterns, heterogeneous spectral characteristics, and dynamic agricultural conditions. Existing methods often suffer from limited spatial modeling capacity, weak generalization across crop types and years. To address these challenges, we propose DFYP, a novel Dynamic Fusion framework for crop Yield Prediction, which combines spectral channel attention, edge-adaptive spatial modeling and a learnable fusion mechanism to improve robustness across diverse agricultural scenarios. Specifically, DFYP introduces three key components: (1) a Resolution-aware Channel Attention (RCA) module that enhances spectral representation by adaptively reweighting input channels based on resolution-specific characteristics; (2) an Adaptive Operator Learning Network (AOL-Net) that dynamically selects operators for convolutional kernels to improve edge-sensitive spatial feature extraction under varying crop and temporal conditions; and (3) a dual-branch architecture with a learnable fusion mechanism, which jointly models local spatial details and global contextual information to support cross-resolution and cross-crop generalization. Extensive experiments on multi-year datasets MODIS and multi-crop dataset Sentinel-2 demonstrate that DFYP consistently outperforms current state-of-the-art baselines in RMSE, MAE, and R2 across different spatial resolutions, crop types, and time periods, showcasing its effectiveness and robustness for real-world agricultural monitoring.

翻译：基于遥感的精确作物产量预测由于复杂的空间模式、异质的光谱特征以及动态的农业条件，仍然是一项具有根本挑战性的任务。现有方法通常存在空间建模能力有限、跨作物类型和年份的泛化能力较弱的问题。为应对这些挑战，我们提出DFYP，一种用于作物产量预测的新型动态融合框架，该框架结合了光谱通道注意力、边缘自适应空间建模以及可学习的融合机制，以提升在不同农业场景下的鲁棒性。具体而言，DFYP引入了三个关键组件：(1) 分辨率感知通道注意力模块，该模块通过基于分辨率特定特性自适应地重新加权输入通道来增强光谱表示；(2) 自适应算子学习网络，该网络动态选择卷积核的算子，以在变化的作物和时序条件下改进对边缘敏感的空间特征提取；(3) 具有可学习融合机制的双分支架构，该架构联合建模局部空间细节和全局上下文信息，以支持跨分辨率和跨作物的泛化。在多年度数据集MODIS和多作物数据集Sentinel-2上的大量实验表明，DFYP在不同空间分辨率、作物类型和时间段上，在RMSE、MAE和R2指标上均持续优于当前最先进的基线方法，展示了其在真实世界农业监测中的有效性和鲁棒性。